POS0618 QUANTITATIVE RADIOMIC RISK SCORE STRATIFIES RESPONSE TO ANTI-FIBROTIC TREATMENT IN A PRECLINICAL LUNG FIBROSIS MODEL
Authors: D. Lauer, H. Gabrys, M. Maciukiewicz, M. Brunner, T. Frauenfelder, S. Tanadini-Lang, M. Heller, A. C. Uldry, K. Klein, O. Distler, J. Schniering, B. Maurer
Journal: Annals of the Rheumatic Diseases (2023)

<h3>Background</h3> Anti-fibrotic drugs aim at slowing disease progression in patients with fibrosing interstitial lung disease (ILD). Currently, only repeated pulmonary function tests and/or serial chest high resolution computed tomography (HRCT) scans spanning long periods can identify treatment responders. Imaging omics data («radiomics») from HRCT scans, which integrate pathophysiological information of the whole lung, may provide a powerful tool not only for prediction of disease progression as shown previously, but also for timely stratification of treatment response on an individual base<sup>[1]</sup>. <h3>Objectives</h3> To explore the potential of HRCT-based radiomics for stratification of treatment response to an established anti-fibrotic drug in a preclinical model of fibrosing ILD. <h3>Methods</h3> Fibrosing ILD was induced in mice by intratracheal instillation of 2 U/kg bleomycin on day 0. Treatment with (a) the (non)receptor tyrosine kinase inhibitor nintedanib at 60 mg/kg q.d. (<i>n</i>=10) or (b) vehicle-only (<i>n</i>=13) q.d. was administered by gavage from day 7 to 21. Lung pre- and post-treatment MicroCT scans were acquired at 35 µm resolution from each mouse. Lung tissue was collected post-mortem on day 21. Radiomic features (<i>n</i>=1'386) were calculated from semi-automatically defined 3D lung volumes using the in-house developed software Z-Rad. Calculation of our composite quantitative radiomic risk score (qRISSc), developed for risk stratification of systemic sclerosis patients with ILD, was performed as previously described<sup>[1]</sup>. Changes between pre- and post-treatment conditions (=delta qRISSc) were used to stratify treatment response. Ashcroft scoring was used to evaluate severity of fibrosis. Markers of fibrosis and inflammation, as well as drug-associated targets, were assessed by gene expression profiling and immunostainings. Lung proteome profiles were acquired by mass spectrometry in data-independent acquisition mode using a nanoLC-MS/MS workflow. Analysis of differentially expressed proteins and pathway enrichment were performed using R packages «limma» and «clusterProfiler», respectively. <h3>Results</h3> Post-treatment, non-omics readouts showed that response to treatment was heterogeneous with varied inter-individual responses. Yet, time-resolved radiomics analysis, capturing the individual pre- and post-treatment disease status, reliably allowed the detection of treatment response. A delta qRISSc threshold was established to distinguish responders from non-responders. Immunostainings of corresponding tissue revealed reduced infiltrates of αSMA+ myofibroblasts and F4/80+ macrophages in responders. In addition, transcriptional levels of fibrotic (<i>Col1a1</i>, <i>Col3a1</i>, <i>Fn1</i>), inflammatory (<i>Ccl2</i>, <i>Il6</i>), and nintedanib-associated (<i>Tgfb1</i>, <i>Timp1</i>, <i>Cxcl1</i>) targets were lower in these samples. Differential protein expression analysis demonstrated that proteins downregulated in responders were enriched in pathways related to fibrosing ILD pathophysiology (Figure 1A). To identify a molecular basis of delta qRISSc, we performed correlation analysis to the proteome profiles of all samples, including controls. Thereby, qRISSc increase (=non-response) strongly correlated with activation of pathways related to fibrosis and nintedanib mode of action (Figure 1B). These results indicate that radiomic signature changes reflect drug response on a molecular level. <h3>Conclusion</h3> Upon clinical validation, HRCT-based radiomics may provide a powerful tool for early distinction of patients benefiting from anti-fibrotic therapy and thus serve as a foundation for a more individualized and improved patient management. <h3>Reference</h3> [1]Schniering J, <i>et al</i>. Eur Respir J 2022; 59(5):2004503. <h3>Acknowledgements:</h3> NIL. <h3>Disclosure of Interests</h3> David Lauer Shareholder of: Roche, Employee of: Roche (until 2020), Hubert Gabrys: None declared, Malgorzata Maciukiewicz: None declared, Matthias Brunner: None declared, Thomas Frauenfelder: None declared, Stephanie Tanadini-Lang: None declared, Manfred Heller: None declared, Anne-Christine Uldry: None declared, Kerstin Klein: None declared, Oliver Distler Speakers bureau: Bayer, Boehringer Ingelheim, Janssen, Medscape, Consultant of: 4P-Pharma, Abbvie, Acceleron, Alcimed, Altavant Siences, Amgen, AnaMar, Arxx, AstraZeneca, Baecon, Blade, Bayer, Boehringer Ingelheim, Corbus, CSL Behring, Galapagos, Glenmark, Horizon, Inventiva, Kymera, Lupin, Miltenyi Biotec, Mitsubishi Tanabe, MSD, Novartis, Prometheus, Redxpharna, Roivant, Sanofi, Topadur, Grant/research support from: Kymera, Mitsubishi Tanabe, Boehringer Ingelheim, Janine Schniering: None declared, Britta Maurer Speakers bureau: Boehringer-Ingelheim, GSK, Novartis. Congress support from Medtalk, Pfizer, Roche, Actelion, Mepha, MSD, Consultant of: Novartis, Boehringer Ingelheim, Janssen-Cilag, GSK, Grant/research support from: AbbVie, Protagen, Novartis Biomedical.